#
#  Copyright 2019 The FATE Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

from fate.arch import Context
from fate.arch.computing.backends.standalone import CSession
from fate.arch.context import Context
from fate.arch.federation.backends.standalone import StandaloneFederation
import pandas as pd
from fate.arch.dataframe import PandasReader
from fate.ml.nn.dataset.table import TableDataset
from fate.ml.glm.homo.lr.client import HomoLRClient
import logging

# Get the root logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
logger.addHandler(ch)


computing = CSession()
ctx = Context(
    "guest",
    computing=computing,
    federation=StandaloneFederation(computing, "fed", ("guest", 10000), [("guest", 10000), ("host", 9999)]),
)

df = pd.read_csv("../../../../../../../examples/data/breast_homo_guest.csv")
df["sample_id"] = [i for i in range(len(df))]

reader = PandasReader(sample_id_name="sample_id", match_id_name="id", label_name="y", dtype="object")
reader_2 = PandasReader(sample_id_name="sample_id", match_id_name="id", dtype="object")
data = reader.to_frame(ctx, df)

# df = data.as_pd_df()
data_2 = reader_2.to_frame(ctx, df.drop(columns=["y"]))
ds = TableDataset(return_dict=True, to_tensor=True)
ds.load(data)


client = HomoLRClient(
    50,
    800,
    optimizer_param={"method": "adam", "penalty": "l1", "aplha": 0.1, "optimizer_para": {"lr": 0.1}},
    init_param={"method": "random", "fill_val": 1.0},
    learning_rate_scheduler={"method": "linear", "scheduler_params": {"start_factor"}},
)
client.l2 = 0.01
client.l1 = 0.01
client.local_mode = True
client.fit(ctx, data, validate_data=data)
export_model = client.get_model()
pred = client.predict(ctx, data)
# pred_2 = client.predict(ctx, data_2)
